sipp-sys 0.1.1

Native llama.cpp FFI layer for Sipp
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#include "../node_context.h"
#include "../op_table.h"
#include "../utils.h"
#include "ggml-impl.h"

#include <cstddef>
#include <memory>
#include <openvino/core/shape.hpp>
#include <openvino/core/strides.hpp>
#include <openvino/op/constant.hpp>
#include <openvino/op/convert.hpp>
#include <openvino/op/extractimagepatches.hpp>
#include <openvino/op/pad.hpp>
#include <openvino/op/reshape.hpp>
#include <openvino/op/transpose.hpp>
#include <openvino/op/util/attr_types.hpp>

namespace ov {
namespace frontend {
namespace ggml {
namespace op {

OutputVector translate_im2col(const NodeContext & context) {
    num_inputs_check(context, 2, 2);
    const int32_t * params = context.get_output_op_params();
    int32_t s0 = params[0];
    int32_t s1 = params[1];
    int32_t p0 = params[2];
    int32_t p1 = params[3];
    int32_t d0 = params[4];
    int32_t d1 = params[5];
    bool is_2D = params[6] == 1;
    ov::Output<Node> res;

    ov::Output<Node> image = context.get_input(1);
    const ov::Shape kernel_shape = context.get_input(0).get_shape();

    const size_t IC = is_2D ? kernel_shape[1] : kernel_shape[2];
    const size_t KH = is_2D ? kernel_shape[2] : 1;
    const size_t KW = kernel_shape[3];

    int32_t stride_w = s0;
    int32_t stride_h = is_2D ? s1 : 1;
    int32_t pad_w = p0;
    int32_t pad_h = is_2D ? p1 : 0;
    int32_t dil_w = d0;
    int32_t dil_h = is_2D ? d1 : 1;

    if (!is_2D) {
        // GGML input shape: [IW, IC, N, 1]
        // OpenVINO input shape: [1, N, IC, IW]
        // Reshape image to: [N, IC, 1, IW]
        const ov::Shape image_shape = image.get_shape();
        const size_t N = image_shape[1];
        const size_t IW = image_shape[3];
        auto image_reshape_shape = ov::op::v0::Constant::create(
            ov::element::i64, ov::Shape{4},
            std::vector<int64_t>{static_cast<int64_t>(N), static_cast<int64_t>(IC), 1, static_cast<int64_t>(IW)});
        image = std::make_shared<ov::op::v1::Reshape>(image, image_reshape_shape, false);
    }

    const ov::Shape patch_sizes = {KH, KW};
    const ov::Strides strides = {static_cast<size_t>(stride_h), static_cast<size_t>(stride_w)};
    const ov::Shape rates = {static_cast<size_t>(dil_h), static_cast<size_t>(dil_w)};

    auto pads_begin =
        ov::op::v0::Constant::create(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, pad_h, pad_w});
    auto pads_end =
        ov::op::v0::Constant::create(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, pad_h, pad_w});

    auto pad = std::make_shared<ov::op::v1::Pad>(image, pads_begin, pads_end, ov::op::PadMode::CONSTANT);
    auto patches =
        std::make_shared<ov::op::v3::ExtractImagePatches>(pad, patch_sizes, strides, rates, ov::op::PadType::VALID);

    // [N, KH*KW*IC, OH, OW] → [N, OH, OW, KH*KW*IC]
    auto perm1 = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
    auto t1 = std::make_shared<ov::op::v1::Transpose>(patches, perm1);

    // [N, OH, OW, KH*KW*IC] → [N, OH, OW, KH*KW, IC]
    const ov::Shape out_shape = t1->get_output_shape(0);
    const size_t N = out_shape[0];
    const size_t OH = out_shape[1];
    const size_t OW = out_shape[2];
    auto reshape1_shape = ov::op::v0::Constant::create(
        ov::element::i64, ov::Shape{5},
        std::vector<int64_t>{static_cast<int64_t>(N), static_cast<int64_t>(OH), static_cast<int64_t>(OW),
                             static_cast<int64_t>(KH * KW), static_cast<int64_t>(IC)});
    auto r1 = std::make_shared<ov::op::v1::Reshape>(t1, reshape1_shape, false);

    // [N, OH, OW, KH*KW, IC] → [N, OH, OW, IC, KH*KW]
    auto perm2 = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{5}, std::vector<int64_t>{0, 1, 2, 4, 3});
    auto t2 = std::make_shared<ov::op::v1::Transpose>(r1, perm2);

    // flatten back to [N, OH, OW, IC*KH*KW]
    auto r2_shape = ov::op::v0::Constant::create(
        ov::element::i64, ov::Shape{4},
        std::vector<int64_t>{static_cast<int64_t>(N), static_cast<int64_t>(OH), static_cast<int64_t>(OW),
                             static_cast<int64_t>(IC * KH * KW)});
    res = std::make_shared<ov::op::v1::Reshape>(t2, r2_shape, false);

    if (!is_2D) {
        // [N, 1, OW, IC * KW] -> [1, N, OW, IC * KW]
        auto final_reshape_shape = ov::op::v0::Constant::create(
            ov::element::i64, ov::Shape{4},
            std::vector<int64_t>{1, static_cast<int64_t>(N), static_cast<int64_t>(OW), static_cast<int64_t>(IC * KW)});
        res = std::make_shared<ov::op::v1::Reshape>(res, final_reshape_shape, false);
    }

    auto output_type = context.get_output_type();
    if (res.get_element_type() != output_type) {
        res = std::make_shared<ov::op::v0::Convert>(res, output_type);
    }

    return rename_outputs_with_suffix({res}, context.get_name());
}

}  // namespace op
}  // namespace ggml
}  // namespace frontend
}  // namespace ov